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Option-Implied Equity Premium Predictions via Entropic TiltinG

Author

Listed:
  • Davide Pettenuzzo

    () (Brandeis University)

  • Konstantinos Metaxoglou

    () (Carleton University)

  • Aaron Smith

    () (University of California, Davis)

Abstract

We propose a new method to improve density forecasts of the equity premium us- ing information from options markets. We tilt the predictive densities from standard econometric models suggested in the stock return predictability literature towards the second moment of the risk-neutral distribution implied by options prices. In so do- ing, we use a simple regression-based approach to remove the variance risk premium. By combining the backward-looking information contained in the econometric models with the forward-looking information from the options prices, tilting yields sharper predictive densities. Using density forecasts of the U.S. equity premium in Rapach and Zhou (2012), we nd that tilting leads to more accurate predictions, both in terms of statistical and economic performance.

Suggested Citation

  • Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99, Brandeis University, Department of Economics and International Businesss School.
  • Handle: RePEc:brd:wpaper:99
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    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP99.pdf
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    References listed on IDEAS

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    1. Back, Kerry, 2010. "Asset Pricing and Portfolio Choice Theory," OUP Catalogue, Oxford University Press, number 9780195380613.
    2. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    3. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
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    6. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    7. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
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    Cited by:

    1. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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